Converting probabilistic tree species range shift projections into meaningful classes for management.

Area of Science:

• Ecology
• Forestry
• Climate Change Science

Background:

• Changing environmental conditions pose challenges for forest management and tree species suitability.
• Predicting tree species distribution under climate change is crucial for conservation and sustainable forestry.

Purpose of the Study:

• To develop and validate an algorithm for classifying tree species suitability based on range shift model outputs.
• To link classified distribution areas to ecological niche characteristics for major European tree species.

Main Methods:

• Developed a classification algorithm for range shift model outputs into four distribution areas: Core, Extended, Occasional, and No occurrence.
• Statistically described and ecologically interpreted these classes using criteria such as population structure and competitive strength.
• Applied a generalized linear model with pan-European presence/absence data and downscaled climate data from a General Circulation Model (GCM).
• Validated the algorithm using the German National Forest Inventory, assessing accuracy with Cohen's Kappa.

Main Results:

• The algorithm successfully classifies tree species distribution areas, linking them to ecological niche qualities.
• Validation showed good agreement for Norway spruce but less accuracy for European beech, suggesting regional factors influence core distributions.
• Identified potential uncertainties arising from diverse modeling approaches and climate scenarios.

Conclusions:

• The algorithm provides a framework for assessing tree species suitability under changing climates, aiding European and regional management.
• Regional factors, such as forest history, may be more influential than climate in core distribution areas for some species.
• Proposed a method to address uncertainty in range shift modeling by considering decision-maker risk attitudes.